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arXiv · 1903.09973

MUSCO: Multi-Stage Compression of neural networks

Julia Gusak·Maksym Kholiavchenko·Evgeny Ponomarev·Larisa Markeeva·Ivan Oseledets·Andrzej Cichocki
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Abstract

The low-rank tensor approximation is very promising for the compression of deep neural networks. We propose a new simple and efficient iterative approach, which alternates low-rank factorization with a smart rank selection and fine-tuning. We demonstrate the efficiency of our method comparing to non-iterative ones. Our approach improves the compression rate while maintaining the accuracy for a variety of tasks.

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Julia Gusak, Maksym Kholiavchenko, Evgeny Ponomarev, Larisa Markeeva, Ivan Oseledets, Andrzej Cichocki. 2019-03-24. MUSCO: Multi-Stage Compression of neural networks. https://arxiv.org/abs/1903.09973

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